Reproduction of selected plant varieties by tissue culture has been a commercial success for many years. The technique has enabled mass production of genetically identical selected ornamental plants, agricultural plants, and forest species. The woody plants in this last group have perhaps posed the greatest challenges. Some success with conifers was achieved in the 1970s using organogenesis techniques wherein a bud, or other organ, was placed on a culture medium where it was ultimately replicated many times. The newly generated buds were placed on a different medium that induced root development. From there, the buds having roots were planted in soil.
While conifer organogenesis was a breakthrough, costs were high due to the large amount of handling needed. There was also some concern about possible genetic modification. It was a decade later before somatic embryogenesis achieved a sufficient success rate so as to become the predominant approach to conifer tissue culture. With somatic embryogenesis, an explant, usually a seed or seed embryo, is placed on an initiation medium where it multiplies into a multitude of genetically identical immature embryos. These can be held in culture for long periods and multiplied to bulk up a particularly desirable clone. Ultimately, the immature embryos are placed on a development or maturation medium where they grow into somatic analogs of mature seed embryos. These embryos are then individually selected and placed on a germination medium for further development. Alternatively, the embryos may be used in manufactured seeds.
There is now a large body of general technical literature and a growing body of patent literature on embryogenesis of plants. Examples of procedures for conifer tissue culture are found in U.S. Pat. Nos. 5,036,007 and 5,236,841, issued to Gupta et al.; U.S. Pat. No. 5,183,757, issued to Roberts; U.S. Pat. No. 5,464,769, issued to Attree et al.; and U.S. Pat. No. 5,563,061, issued to Gupta.
One of the more labor intensive and subjective steps in the embryogenesis procedure is the selection from the maturation medium of individual embryos suitable for germination. The embryos may be present in a number of stages of maturity and development. Those that are most likely to successfully germinate into normal plants are preferentially selected using a number of visually evaluated screening criteria. Morphological features such as axial symmetry, cotyledon development, surface texture, color, and others are examined and applied as a pass/fail test before the embryos are passed on for germination. This is a skilled yet tedious job that is time consuming and expensive. Further, it poses a major production bottleneck when the ultimate desired output will be in the millions of plants.
It has been proposed to use some form of instrumental image analysis for embryo selection to replace the visual evaluation described above. For examples, refer to Cheng, Z., and P. P. Ling, “Machine vision techniques for somatic coffee embryo morphological feature extraction,” Trans. Amer. Soc. Agri. Eng. 37:1663-1669 (1994), or Chi, C. M., C. Zhang, E. J. Staba, T. J. Cooke, and W-S. Hu, “An advanced image analysis system for evaluation of somatic embryo development,” Biotech. and Bioeng 50:65-72 (1996). All of these methods require considerable pre-judgment of which morphological features are important and the development of mathematical methods to extract this information from the images. Relatively little of the information from the image has actually been used.
The problem of how to best use image analysis to automate the selection of somatic embryos after they had been separated from residual tissue, singulated, and imaged in color from multiple positions has not been successfully addressed. Various methods are known for extracting size and shape information from scanned images. As one example, Moghaddam et al., U.S. Pat. No. 5,710,833, describes a method useful for recognition of any multi-featured entity such as a human face. U.S. Pat. No. 5,590,261, issued to Sclaroff et al., describes a method that can be used for object recognition purposes.
Where embryos are concerned, a further problem using scanning technology is that morphology differs between clones within a given species. The differences between acceptable and rejected embryos can be very subtle, varying by clone. Hence, the choice of selection criteria for machine use tends to be subjective, difficult to specify mathematically, and may be clone specific.
The development of high-speed computers and new spectroscopic hardware has led to the development of new instruments which have the capability to rapidly acquire spectra on large numbers of samples. However, the acquisition of vast amounts of spectral data from a sample necessitates the development of similarly powerful data analysis tools to uncover subtle relationships between the collected spectra and the chemical properties of the sample. One such data analysis methodology, commonly known as chemometrics, applies multivariate statistical techniques to complex chemical systems in order to facilitate the discovery of the relationship between the absorption, transmittance or reflectance spectral data acquired from a sample and some specified property of the sample that is subject to independent measurement. The end result of multivariate analysis is the development of a predictive classification model that allows new samples of unknown properties to be rapidly and accurately classified according to a specified property based upon the acquired spectral data. For example, multivariate analysis techniques, such as: principal component analysis (PCA) and a principal component-based method, projection to latent structures (PLS), have been used to explore the multivariate information in previous applications of near-infrared (NIR) spectroscopy to the pulp and paper industry to develop classification models for paper quality. See, for example, U.S. Pat. Nos. 5,638,284, 5,680,320, 5,680,321, and 5,842,150.
Therefore, a continuing need exists for imaging systems and methods to capture spectral data from a biological sample and to use the collected spectra to develop classification models for the biological samples.